Computes a dense optical flow using the Gunnar Farneback's algorithm

flow = cv.calcOpticalFlowFarneback(prevImg, nextImg)
flow = cv.calcOpticalFlowFarneback(prevImg, nextImg, 'OptionName',optionValue, ...)

Input

• prevImg First 8-bit single-channel input image.
• nextImg Second input image of the same size and the same type as prevImg.

Output

• flow Computed flow image that has the same size as prevImg and single type (2-channels). Flow for (x,y) is stored in the third dimension.

Options

• InitialFlow Initial flow approximation. Not set by default.
• PyrScale Parameter specifying the image scale (<1) to build pyramids for each image. PyrScale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous one. default 0.5.
• Levels Number of pyramid layers including the initial image. Levels=1 means that no extra layers are created and only the original images are used. default 5.
• WinSize Averaging window size. Larger values increase the algorithm robustness to image noise and give more chances for fast motion detection, but yield more blurred motion field. default 13.
• Iterations Number of iterations the algorithm does at each pyramid level. default 10.
• PolyN Size of the pixel neighborhood used to find polynomial expansion in each pixel. Larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field. Typically, PolyN is 5 or 7. default 5.
• PolySigma Standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion. For PolyN=5, you can set PolySigma = 1.1. For PolyN=7, a good value would be PolySigma = 1.5. default 1.1.
• Gaussian Use the Gaussian WinSize x WinSize filter instead of a box filter of the same size for optical flow estimation. Usually, this option gives z more accurate flow than with a box filter, at the cost of lower speed. Normally, WinSize for a Gaussian window should be set to a larger value to achieve the same level of robustness. default false.

The function finds an optical flow for each prevImg pixel using the [Farneback2003] alorithm so that:

prevImg(y,x) ~ nextImg(y + flow(y,x,2), x + flow(y,x,1))

References

[Farneback2003]:

Gunnar Farneback. "Two-frame motion estimation based on polynomial expansion". In Image Analysis, pages 363-370. Springer, 2003.